图书情报工作 ›› 2016, Vol. 60 ›› Issue (9): 55-60.DOI: 10.13266/j.issn.0252-3116.2016.09.008

• 工作研究 • 上一篇    下一篇

“211工程”高校图书馆馆藏资源推荐系统调查探析

李民1, 王颖纯1, 刘燕权2   

  1. 1. 天津理工大学管理学院 天津 300384;
    2. 美国南康涅狄格州立大学信息与图书馆学系 纽黑文 06515
  • 收稿日期:2016-03-09 修回日期:2016-04-22 出版日期:2016-05-05 发布日期:2016-05-05
  • 作者简介:李民(ORCID:0000-0001-7722-4633),硕士研究生,E-mail:liminhapyy@163.com;王颖纯(ORCID:0000-0003-4280-1896),教授,硕士;刘燕权(ORCID:0000-0002-1486-8762),终身教授。

An Analytical Survey of Recommender Systems in China's “211 Project” University Libraries

Li Min1, Wang Yingchun1, Liu Yanquan2   

  1. 1. School of Management, Tianjin University of Technology, Tianjin 300384;
    2. Department of Info & Lib Science, Southern Connecticut State University, New Haven, 06515
  • Received:2016-03-09 Revised:2016-04-22 Online:2016-05-05 Published:2016-05-05

摘要: [目的/意义]调研推荐系统在高校图书馆中的应用现状及存在的问题,为增强图书馆对知识信息的智能处理能力提供参考依据。[方法/过程]通过对国内116所"211工程"院校进行网站访查和问卷调查,用定量分析与定性分析相结合等方法,对调查结果进行归类、分类统计和对比分析。[结果/结论]研究发现,受访高校图书馆均提供非个性化推荐服务,63%受访高校提供个性化推荐服务;推荐服务内容丰富、方式多样、形式各异,79%的高校积极寻求与其他平台的合作,拓宽推荐深度和广度。存在的问题包括:图书馆推荐系统个性化程度不高,过于依赖图书管理集成系统所附带的推荐功能,不够系统化、智慧化;推荐系统满意度有待提高,有相当多的用户担心推荐系统会泄露个人隐私。

关键词: 推荐系统, 个性化推荐, 智慧图书馆, 推荐服务, 知识服务

Abstract: [Purpose/significance] Studies on recommender systems in the library field have been mostly focused on theoretical models or technical constructions. They lack data of the current status and real practical issues. This paper aims to investigate the application situation and existing problems of the recommender system in university libraries in order to put forward references for enhancing libraries' knowledge services. [Method/process] Based on website visits and the questionnaire survey of 116 universities included in the "211 Project", data were processed through classified statistics and comparative analysis. [Result/conclusion] The results show that almost all the university libraries offer non-personalized recommender services, and 63% of them provide personalized recommender services. Various recommender systems with diverse services are widely emerging, which reflects great needs for smart knowledge discovery and delivery services. 79% of the university libraries actively seek cooperation with other platforms to broaden their recommending capacities. More personalized recommender systems and systematic and intelligent services are still in demands. In addition, libraries need to pay more attention to readers' satisfaction on the recommender systems, as well as their privacy issues.

Key words: recommender system, personalized recommendation, smart library, recommender service, knowledge services

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